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《中华肺部疾病杂志》[ISSN:1006-6977/CN:61-1281/TN]

卷:

期数:

2025年02期

页码:

213-219

栏目:

论著

出版日期:

2025-04-25

文章信息/Info

Title:

Comparative analysis and predictive modeling of clinical, pathologic, and dual-energy CT parameters for tumor spread through air spaces in stage I non-small cell lung cancer

作者:

徐俊洁; 罗 虎; 陶锡鹏; 谢李词; 周向东

400038 重庆,陆军(第三)军医大学第一附属医院呼吸与危重症医学科

Author(s):

Xu Junjie;  Luo Hu;  Tao Xipeng;  Xie Lici;  Zhou Xiangdong

Department of Respiratory and Critical Care Medicine, First Affiliated Hospital of Army Medical University, Chongqing 400038, China

关键词:

非小细胞肺癌;  经气腔播散;  双能量计算机体层成像;  预测模型

Keywords:

Non-small cell lung cance;  Spread through air spaces;  Dual-energy CT;  Prediction model

分类号:

R734.2

DOI:

10.3877/cma.j.issn.1674-6902.2025.02.002

摘要:

目的 比较Ⅰ期非小细胞肺癌(non small cell lung cancer, NSCLC)通过气腔播散(spread through air spaces, STAS)术前临床、病理特征与双能量CT(dual-energy CT, DECT)影像学参数,构建预测STAS的列线图模型。方法 纳入2016年1月至2024年9月经术后病理确诊的Ⅰ期NSCLC患者269例为对象,其中STAS阳性100例、阴性169例。收集两组NSCLC患者的临床资料、术前DECT影像学资料、病理参数进行统计分析。采用LASSO-logistic回归筛选预测因子并构建列线图模型。通过受试者工作特征(receiver operating characteristic, ROC)曲线的曲线下面积(area under curve, AUC)、校准图、临床决策曲线(decision curve Analysis, DCA)与临床影响曲线(clinical impact curve, CIC)分析模型的性能。选取年龄、性别、吸烟史、糖尿病、高血压、肿瘤位置和TNM分期为协变量进行1︰1倾向性得分匹配(propensity score matching, PSM),共匹配出STAS阳性86例、阴性86例进行STAS与术后病理特征分析。结果 单因素分析显示阳性与阴性者吸烟史、肺气肿、胸膜牵拉征、毛刺征、结节性质、实性成分占比(consolidation-to-tumour ratio, CTR)、RECIST直径、小结节体积、碘比值及平均CT值有统计学差异(P<0.05)。经多因素分析表明肺气肿背景、毛刺征、CTR、碘比值及CT值是Ⅰ期NSCLC患者STAS阳性预测因子。预测模型结果显示ROC曲线AUC为0.800(95%CI:0.744～0.854),准确度=75.1%,灵敏度=78%,特异性=73.4%,阳性预测值=63.4%,阴性预测值=84.9%,F1评分=0.699。使用R软件所构建的Nomogram列线图将预测模型可视化,建立的校正曲线与理想曲线有重合度,CIC与DCA曲线展现出临床应用价值。PSM术后病理结果分析显示STAS与高侵袭性病理特征相关,容易有血管浸润(7.0% vs. 0.0%, P=0.029)和脉管内癌栓(11.6% vs. 1.2%, P=0.013),有高的Ki-67水平和IASLC分级(P<0.05)。 结论 以术前DECT影像学中的肺气肿背景、毛刺征、CTR、碘比值及平均CT值为基础构建的Ⅰ期NSCLC患者的临床预测模型具有诊断预测意义,结合三维成像特征应用于临床Ⅰ期NSCLC的STAS术前评估; STAS与血流丰富和高侵袭性病理特征相关。

Abstract:

Objective To compare the preoperative clinical and pathological characteristics of stage I non-small cell lung cancer(NSCLC)with spread through air spaces(STAS)using dual-energy CT(DECT)imaging parameters and construct a nomogram model for predicting STAS. Methods A total of 269 patients with pathologically confirmed stage I NSCLC from January 2016 to September 2024 were enrolled, including 100 STAS-positive and 169 STAS-negative cases. Clinical data, preoperative DECT imaging parameters, and pathological features were analyzed. LASSO-logistic regression was used to screen predictors and develop the nomogram. Model performance was evaluated via receiver operating characteristic(ROC)curve analysis(area under the curve, AUC), calibration plots, decision curve analysis(DCA), and clinical impact curve(CIC). Propensity score matching(PSM, 1︰1)adjusted for age, sex, smoking history, diabetes, hypertension, tumor location, and TNM stage yielded 86 STAS-positive and 86 STAS-negative cases for postoperative pathological analysis. Results Univariate analysis revealed significant differences in smoking history, emphysema, pleural retraction, spiculation, nodule type, consolidation-to-tumor ratio(CTR), RECIST diameter, small nodule volume, iodine ratio, and mean CT value between STAS-positive and-negative groups(P<0.05). Multivariate analysis identified emphysema background, spiculation, CTR, iodine ratio, and CT value as independent predictors of STAS. The nomogram model achieved an AUC of 0.800(95%CI: 0.744～0.854), accuracy=75.1%, sensitivity=78%, specificity=73.4%, positive predictive value=63.4%, negative predictive value=84.9%, and F1-score=0.699. Calibration curves showed good agreement with ideal predictions, while DCA and CIC demonstrated clinical utility. Post-PSM analysis indicated that STAS correlated with aggressive pathological features, including vascular invasion(7.0% vs. 0.0%, P=0.029), intravascular tumor thrombus(11.6% vs. 1.2%, P=0.013), elevated Ki-67 levels, and higher IASLC grading(P<0.05). Conclusion The clinical prediction model based on preoperative DECT parameters(emphysema background, spiculation, CTR, iodine ratio, and mean CT value)provides diagnostic value for preoperative STAS assessment in stage I NSCLC when combined with three-dimensional imaging features. STAS is associated with abundant blood flow and highly invasive pathological characteristics.

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备注/Memo

备注/Memo:

基金项目: 重庆市适宜技术推广项目(No.2025jstg015)
通信作者: 周向东,Email: xiangdongzhou@126.com

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更新日期/Last Update: 2025-04-25